Overview

Dataset statistics

Number of variables16
Number of observations46428
Missing cells18400
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 MiB
Average record size in memory136.0 B

Variable types

Numeric10
Text3
Categorical2
DateTime1

Alerts

host_id is highly overall correlated with idHigh correlation
id is highly overall correlated with host_idHigh correlation
latitude is highly overall correlated with neighbourhood_groupHigh correlation
longitude is highly overall correlated with neighbourhood_groupHigh correlation
neighbourhood_group is highly overall correlated with latitude and 1 other fieldsHigh correlation
number_of_reviews is highly overall correlated with reviews_per_monthHigh correlation
price is highly overall correlated with room_typeHigh correlation
reviews_per_month is highly overall correlated with number_of_reviewsHigh correlation
room_type is highly overall correlated with priceHigh correlation
last_review has 9182 (19.8%) missing valuesMissing
reviews_per_month has 9182 (19.8%) missing valuesMissing
id has unique valuesUnique
minimum_nights has 12148 (26.2%) zerosZeros
number_of_reviews has 9182 (19.8%) zerosZeros
availability_365 has 17005 (36.6%) zerosZeros

Reproduction

Analysis started2024-06-19 19:02:05.850946
Analysis finished2024-06-19 19:02:41.342232
Duration35.49 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct46428
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18918078
Minimum2539
Maximum36487245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size725.4 KiB
2024-06-19T19:02:41.533610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2539
5-th percentile1209898.9
Q19445461.2
median19544622
Q328937774
95-th percentile35225452
Maximum36487245
Range36484706
Interquartile range (IQR)19492312

Descriptive statistics

Standard deviation10931202
Coefficient of variation (CV)0.5778178
Kurtosis-1.2191019
Mean18918078
Median Absolute Deviation (MAD)9799792.5
Skewness-0.080743829
Sum8.7832853 × 1011
Variance1.1949118 × 1014
MonotonicityStrictly increasing
2024-06-19T19:02:41.849370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2539 1
 
< 0.1%
25308563 1
 
< 0.1%
25309799 1
 
< 0.1%
25310242 1
 
< 0.1%
25310404 1
 
< 0.1%
25310497 1
 
< 0.1%
25311740 1
 
< 0.1%
25312773 1
 
< 0.1%
25313204 1
 
< 0.1%
25313748 1
 
< 0.1%
Other values (46418) 46418
> 99.9%
ValueCountFrequency (%)
2539 1
< 0.1%
2595 1
< 0.1%
3647 1
< 0.1%
3831 1
< 0.1%
5022 1
< 0.1%
5099 1
< 0.1%
5121 1
< 0.1%
5178 1
< 0.1%
5203 1
< 0.1%
5238 1
< 0.1%
ValueCountFrequency (%)
36487245 1
< 0.1%
36485609 1
< 0.1%
36485431 1
< 0.1%
36485057 1
< 0.1%
36484665 1
< 0.1%
36484363 1
< 0.1%
36484087 1
< 0.1%
36483152 1
< 0.1%
36483010 1
< 0.1%
36482809 1
< 0.1%

name
Text

Distinct45489
Distinct (%)98.0%
Missing15
Missing (%)< 0.1%
Memory size725.4 KiB
2024-06-19T19:02:42.408752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length179
Median length73
Mean length36.76735
Min length1

Characters and Unicode

Total characters1706483
Distinct characters768
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44878 ?
Unique (%)96.7%

Sample

1st rowClean & quiet apt home by the park
2nd rowSkylit Midtown Castle
3rd rowTHE VILLAGE OF HARLEM....NEW YORK !
4th rowCozy Entire Floor of Brownstone
5th rowEntire Apt: Spacious Studio/Loft by central park
ValueCountFrequency (%)
in 16203
 
5.7%
room 9976
 
3.5%
7815
 
2.8%
bedroom 7283
 
2.6%
private 7022
 
2.5%
apartment 6461
 
2.3%
cozy 4946
 
1.8%
apt 4410
 
1.6%
brooklyn 3918
 
1.4%
studio 3897
 
1.4%
Other values (11921) 210490
74.5%
2024-06-19T19:02:43.375199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
237569
 
13.9%
e 117726
 
6.9%
o 116911
 
6.9%
t 100052
 
5.9%
a 98692
 
5.8%
r 93300
 
5.5%
i 90377
 
5.3%
n 90086
 
5.3%
l 48969
 
2.9%
m 47334
 
2.8%
Other values (758) 665467
39.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1706483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
237569
 
13.9%
e 117726
 
6.9%
o 116911
 
6.9%
t 100052
 
5.9%
a 98692
 
5.8%
r 93300
 
5.5%
i 90377
 
5.3%
n 90086
 
5.3%
l 48969
 
2.9%
m 47334
 
2.8%
Other values (758) 665467
39.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1706483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
237569
 
13.9%
e 117726
 
6.9%
o 116911
 
6.9%
t 100052
 
5.9%
a 98692
 
5.8%
r 93300
 
5.5%
i 90377
 
5.3%
n 90086
 
5.3%
l 48969
 
2.9%
m 47334
 
2.8%
Other values (758) 665467
39.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1706483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
237569
 
13.9%
e 117726
 
6.9%
o 116911
 
6.9%
t 100052
 
5.9%
a 98692
 
5.8%
r 93300
 
5.5%
i 90377
 
5.3%
n 90086
 
5.3%
l 48969
 
2.9%
m 47334
 
2.8%
Other values (758) 665467
39.0%

host_id
Real number (ℝ)

HIGH CORRELATION 

Distinct35770
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66451005
Minimum2438
Maximum2.7432131 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size725.4 KiB
2024-06-19T19:02:43.840256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2438
5-th percentile807362.4
Q17719136.2
median30321518
Q31.0564047 × 108
95-th percentile2.3890395 × 108
Maximum2.7432131 × 108
Range2.7431888 × 108
Interquartile range (IQR)97921335

Descriptive statistics

Standard deviation77691273
Coefficient of variation (CV)1.1691512
Kurtosis0.26563043
Mean66451005
Median Absolute Deviation (MAD)27027080
Skewness1.2359882
Sum3.0851873 × 1012
Variance6.0359339 × 1015
MonotonicityNot monotonic
2024-06-19T19:02:44.324989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219517861 272
 
0.6%
107434423 192
 
0.4%
137358866 103
 
0.2%
30283594 98
 
0.2%
12243051 95
 
0.2%
16098958 91
 
0.2%
61391963 91
 
0.2%
22541573 87
 
0.2%
7503643 52
 
0.1%
1475015 52
 
0.1%
Other values (35760) 45295
97.6%
ValueCountFrequency (%)
2438 1
 
< 0.1%
2571 1
 
< 0.1%
2787 6
< 0.1%
2845 2
 
< 0.1%
2868 1
 
< 0.1%
2881 2
 
< 0.1%
3151 1
 
< 0.1%
3211 1
 
< 0.1%
3415 1
 
< 0.1%
3563 1
 
< 0.1%
ValueCountFrequency (%)
274321313 1
< 0.1%
274311461 1
< 0.1%
274307600 1
< 0.1%
274298453 1
< 0.1%
274273284 1
< 0.1%
274225617 1
< 0.1%
274195458 1
< 0.1%
274188386 1
< 0.1%
274103383 1
< 0.1%
274040642 1
< 0.1%
Distinct11081
Distinct (%)23.9%
Missing21
Missing (%)< 0.1%
Memory size725.4 KiB
2024-06-19T19:02:45.063256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length35
Median length31
Mean length6.1095955
Min length1

Characters and Unicode

Total characters283528
Distinct characters199
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6646 ?
Unique (%)14.3%

Sample

1st rowJohn
2nd rowJennifer
3rd rowElisabeth
4th rowLisaRoxanne
5th rowLaura
ValueCountFrequency (%)
1055
 
2.0%
and 589
 
1.1%
michael 438
 
0.8%
david 416
 
0.8%
sonder 367
 
0.7%
john 319
 
0.6%
alex 309
 
0.6%
laura 284
 
0.5%
nyc 282
 
0.5%
maria 234
 
0.5%
Other values (9966) 47391
91.7%
2024-06-19T19:02:46.383426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 36141
 
12.7%
e 27173
 
9.6%
i 23124
 
8.2%
n 22810
 
8.0%
r 16949
 
6.0%
l 14519
 
5.1%
o 12101
 
4.3%
t 8937
 
3.2%
s 8673
 
3.1%
h 8593
 
3.0%
Other values (189) 104508
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 283528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 36141
 
12.7%
e 27173
 
9.6%
i 23124
 
8.2%
n 22810
 
8.0%
r 16949
 
6.0%
l 14519
 
5.1%
o 12101
 
4.3%
t 8937
 
3.2%
s 8673
 
3.1%
h 8593
 
3.0%
Other values (189) 104508
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 283528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 36141
 
12.7%
e 27173
 
9.6%
i 23124
 
8.2%
n 22810
 
8.0%
r 16949
 
6.0%
l 14519
 
5.1%
o 12101
 
4.3%
t 8937
 
3.2%
s 8673
 
3.1%
h 8593
 
3.0%
Other values (189) 104508
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 283528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 36141
 
12.7%
e 27173
 
9.6%
i 23124
 
8.2%
n 22810
 
8.0%
r 16949
 
6.0%
l 14519
 
5.1%
o 12101
 
4.3%
t 8937
 
3.2%
s 8673
 
3.1%
h 8593
 
3.0%
Other values (189) 104508
36.9%

neighbourhood_group
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size725.4 KiB
Manhattan
19855 
Brooklyn
19550 
Queens
5586 
Bronx
 
1072
Staten Island
 
365

Length

Max length13
Median length9
Mean length8.1570604
Min length5

Characters and Unicode

Total characters378716
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrooklyn
2nd rowManhattan
3rd rowManhattan
4th rowBrooklyn
5th rowManhattan

Common Values

ValueCountFrequency (%)
Manhattan 19855
42.8%
Brooklyn 19550
42.1%
Queens 5586
 
12.0%
Bronx 1072
 
2.3%
Staten Island 365
 
0.8%

Length

2024-06-19T19:02:46.915593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-19T19:02:47.300554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
manhattan 19855
42.4%
brooklyn 19550
41.8%
queens 5586
 
11.9%
bronx 1072
 
2.3%
staten 365
 
0.8%
island 365
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n 66648
17.6%
a 60295
15.9%
t 40440
10.7%
o 40172
10.6%
B 20622
 
5.4%
r 20622
 
5.4%
l 19915
 
5.3%
M 19855
 
5.2%
h 19855
 
5.2%
y 19550
 
5.2%
Other values (10) 50742
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 378716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 66648
17.6%
a 60295
15.9%
t 40440
10.7%
o 40172
10.6%
B 20622
 
5.4%
r 20622
 
5.4%
l 19915
 
5.3%
M 19855
 
5.2%
h 19855
 
5.2%
y 19550
 
5.2%
Other values (10) 50742
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 378716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 66648
17.6%
a 60295
15.9%
t 40440
10.7%
o 40172
10.6%
B 20622
 
5.4%
r 20622
 
5.4%
l 19915
 
5.3%
M 19855
 
5.2%
h 19855
 
5.2%
y 19550
 
5.2%
Other values (10) 50742
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 378716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 66648
17.6%
a 60295
15.9%
t 40440
10.7%
o 40172
10.6%
B 20622
 
5.4%
r 20622
 
5.4%
l 19915
 
5.3%
M 19855
 
5.2%
h 19855
 
5.2%
y 19550
 
5.2%
Other values (10) 50742
13.4%
Distinct219
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size725.4 KiB
2024-06-19T19:02:47.720800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length26
Median length17
Mean length11.925993
Min length4

Characters and Unicode

Total characters553700
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowKensington
2nd rowMidtown
3rd rowHarlem
4th rowClinton Hill
5th rowEast Harlem
ValueCountFrequency (%)
east 6298
 
8.4%
side 4376
 
5.8%
williamsburg 3771
 
5.0%
harlem 3693
 
4.9%
bedford-stuyvesant 3647
 
4.9%
upper 3506
 
4.7%
heights 3504
 
4.7%
village 2905
 
3.9%
west 2502
 
3.3%
bushwick 2442
 
3.3%
Other values (231) 38317
51.1%
2024-06-19T19:02:48.485142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 50730
 
9.2%
i 39813
 
7.2%
s 37999
 
6.9%
t 36585
 
6.6%
a 35885
 
6.5%
l 32481
 
5.9%
r 32219
 
5.8%
28533
 
5.2%
n 24857
 
4.5%
o 22938
 
4.1%
Other values (44) 211660
38.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 553700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 50730
 
9.2%
i 39813
 
7.2%
s 37999
 
6.9%
t 36585
 
6.6%
a 35885
 
6.5%
l 32481
 
5.9%
r 32219
 
5.8%
28533
 
5.2%
n 24857
 
4.5%
o 22938
 
4.1%
Other values (44) 211660
38.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 553700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 50730
 
9.2%
i 39813
 
7.2%
s 37999
 
6.9%
t 36585
 
6.6%
a 35885
 
6.5%
l 32481
 
5.9%
r 32219
 
5.8%
28533
 
5.2%
n 24857
 
4.5%
o 22938
 
4.1%
Other values (44) 211660
38.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 553700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 50730
 
9.2%
i 39813
 
7.2%
s 37999
 
6.9%
t 36585
 
6.6%
a 35885
 
6.5%
l 32481
 
5.9%
r 32219
 
5.8%
28533
 
5.2%
n 24857
 
4.5%
o 22938
 
4.1%
Other values (44) 211660
38.2%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct18791
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.728572
Minimum40.49979
Maximum40.91306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size725.4 KiB
2024-06-19T19:02:48.801291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum40.49979
5-th percentile40.64538
Q140.68936
median40.72201
Q340.76333
95-th percentile40.826463
Maximum40.91306
Range0.41327
Interquartile range (IQR)0.07397

Descriptive statistics

Standard deviation0.055190472
Coefficient of variation (CV)0.00135508
Kurtosis0.099724991
Mean40.728572
Median Absolute Deviation (MAD)0.03641
Skewness0.25836604
Sum1890946.1
Variance0.0030459882
MonotonicityNot monotonic
2024-06-19T19:02:49.078881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.71813 18
 
< 0.1%
40.68444 13
 
< 0.1%
40.68634 13
 
< 0.1%
40.69414 13
 
< 0.1%
40.71171 12
 
< 0.1%
40.68537 12
 
< 0.1%
40.71353 12
 
< 0.1%
40.7191 11
 
< 0.1%
40.69054 11
 
< 0.1%
40.71923 11
 
< 0.1%
Other values (18781) 46302
99.7%
ValueCountFrequency (%)
40.49979 1
< 0.1%
40.50641 1
< 0.1%
40.50708 1
< 0.1%
40.50868 1
< 0.1%
40.50873 1
< 0.1%
40.50943 1
< 0.1%
40.51133 1
< 0.1%
40.52211 1
< 0.1%
40.52293 1
< 0.1%
40.527 1
< 0.1%
ValueCountFrequency (%)
40.91306 1
< 0.1%
40.91234 1
< 0.1%
40.91169 1
< 0.1%
40.91167 1
< 0.1%
40.90804 1
< 0.1%
40.90734 1
< 0.1%
40.90527 1
< 0.1%
40.90484 1
< 0.1%
40.90406 1
< 0.1%
40.90391 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct14563
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.950968
Minimum-74.24442
Maximum-73.71299
Zeros0
Zeros (%)0.0%
Negative46428
Negative (%)100.0%
Memory size725.4 KiB
2024-06-19T19:02:49.390072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-74.24442
5-th percentile-74.00326
Q1-73.9821
median-73.95457
Q3-73.934628
95-th percentile-73.86389
Maximum-73.71299
Range0.53143
Interquartile range (IQR)0.0474725

Descriptive statistics

Standard deviation0.046385832
Coefficient of variation (CV)-0.00062725119
Kurtosis4.9394791
Mean-73.950968
Median Absolute Deviation (MAD)0.02488
Skewness1.2497163
Sum-3433395.5
Variance0.0021516454
MonotonicityNot monotonic
2024-06-19T19:02:49.699282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.95677 18
 
< 0.1%
-73.95427 17
 
< 0.1%
-73.95332 16
 
< 0.1%
-73.95136 16
 
< 0.1%
-73.9506 16
 
< 0.1%
-73.94791 16
 
< 0.1%
-73.95405 16
 
< 0.1%
-73.95725 15
 
< 0.1%
-73.98439 15
 
< 0.1%
-73.95742 15
 
< 0.1%
Other values (14553) 46268
99.7%
ValueCountFrequency (%)
-74.24442 1
< 0.1%
-74.24285 1
< 0.1%
-74.24084 1
< 0.1%
-74.23986 1
< 0.1%
-74.23914 1
< 0.1%
-74.23803 1
< 0.1%
-74.23059 1
< 0.1%
-74.21238 1
< 0.1%
-74.21017 1
< 0.1%
-74.20941 1
< 0.1%
ValueCountFrequency (%)
-73.71299 1
< 0.1%
-73.7169 1
< 0.1%
-73.71795 1
< 0.1%
-73.71829 1
< 0.1%
-73.71928 1
< 0.1%
-73.72173 1
< 0.1%
-73.72179 1
< 0.1%
-73.72247 1
< 0.1%
-73.72435 1
< 0.1%
-73.72581 1
< 0.1%

room_type
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size725.4 KiB
Entire home/apt
23252 
Private room
22036 
Shared room
 
1140

Length

Max length15
Median length15
Mean length13.477901
Min length11

Characters and Unicode

Total characters625752
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowPrivate room
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 23252
50.1%
Private room 22036
47.5%
Shared room 1140
 
2.5%

Length

2024-06-19T19:02:49.985727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-19T19:02:50.247704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
entire 23252
25.0%
home/apt 23252
25.0%
room 23176
25.0%
private 22036
23.7%
shared 1140
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 69680
11.1%
o 69604
11.1%
r 69604
11.1%
t 68540
11.0%
a 46428
 
7.4%
46428
 
7.4%
m 46428
 
7.4%
i 45288
 
7.2%
h 24392
 
3.9%
p 23252
 
3.7%
Other values (7) 116108
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 625752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 69680
11.1%
o 69604
11.1%
r 69604
11.1%
t 68540
11.0%
a 46428
 
7.4%
46428
 
7.4%
m 46428
 
7.4%
i 45288
 
7.2%
h 24392
 
3.9%
p 23252
 
3.7%
Other values (7) 116108
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 625752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 69680
11.1%
o 69604
11.1%
r 69604
11.1%
t 68540
11.0%
a 46428
 
7.4%
46428
 
7.4%
m 46428
 
7.4%
i 45288
 
7.2%
h 24392
 
3.9%
p 23252
 
3.7%
Other values (7) 116108
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 625752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 69680
11.1%
o 69604
11.1%
r 69604
11.1%
t 68540
11.0%
a 46428
 
7.4%
46428
 
7.4%
m 46428
 
7.4%
i 45288
 
7.2%
h 24392
 
3.9%
p 23252
 
3.7%
Other values (7) 116108
18.6%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct337
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.53802
Minimum10
Maximum350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size725.4 KiB
2024-06-19T19:02:50.488409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile40
Q165
median100
Q3160
95-th percentile275
Maximum350
Range340
Interquartile range (IQR)95

Descriptive statistics

Standard deviation71.862581
Coefficient of variation (CV)0.58645132
Kurtosis0.5184784
Mean122.53802
Median Absolute Deviation (MAD)44
Skewness1.0306644
Sum5689195
Variance5164.2306
MonotonicityNot monotonic
2024-06-19T19:02:50.792740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 2051
 
4.4%
150 2047
 
4.4%
50 1534
 
3.3%
60 1458
 
3.1%
200 1401
 
3.0%
75 1370
 
3.0%
80 1272
 
2.7%
65 1190
 
2.6%
70 1170
 
2.5%
120 1130
 
2.4%
Other values (327) 31805
68.5%
ValueCountFrequency (%)
10 17
< 0.1%
11 3
 
< 0.1%
12 4
 
< 0.1%
13 1
 
< 0.1%
15 6
 
< 0.1%
16 6
 
< 0.1%
18 2
 
< 0.1%
19 4
 
< 0.1%
20 33
0.1%
21 6
 
< 0.1%
ValueCountFrequency (%)
350 381
0.8%
349 45
 
0.1%
348 3
 
< 0.1%
347 4
 
< 0.1%
346 2
 
< 0.1%
345 25
 
0.1%
344 1
 
< 0.1%
343 4
 
< 0.1%
342 1
 
< 0.1%
341 4
 
< 0.1%

minimum_nights
Real number (ℝ)

ZEROS 

Distinct107
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1295348
Minimum0
Maximum7.1308988
Zeros12148
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size725.4 KiB
2024-06-19T19:02:51.103998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.69314718
Q31.6094379
95-th percentile3.4011974
Maximum7.1308988
Range7.1308988
Interquartile range (IQR)1.6094379

Descriptive statistics

Standard deviation1.0691072
Coefficient of variation (CV)0.94650216
Kurtosis0.84951091
Mean1.1295348
Median Absolute Deviation (MAD)0.69314718
Skewness1.1145044
Sum52442.043
Variance1.1429901
MonotonicityNot monotonic
2024-06-19T19:02:51.395850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12148
26.2%
0.6931471806 11199
24.1%
1.098612289 7506
16.2%
3.401197382 3534
 
7.6%
1.386294361 3106
 
6.7%
1.609437912 2854
 
6.1%
1.945910149 1975
 
4.3%
1.791759469 694
 
1.5%
2.63905733 543
 
1.2%
2.302585093 464
 
1.0%
Other values (97) 2405
 
5.2%
ValueCountFrequency (%)
0 12148
26.2%
0.6931471806 11199
24.1%
1.098612289 7506
16.2%
1.386294361 3106
 
6.7%
1.609437912 2854
 
6.1%
1.791759469 694
 
1.5%
1.945910149 1975
 
4.3%
2.079441542 129
 
0.3%
2.197224577 79
 
0.2%
2.302585093 464
 
1.0%
ValueCountFrequency (%)
7.13089883 1
 
< 0.1%
6.906754779 3
 
< 0.1%
6.214608098 5
 
< 0.1%
6.173786104 1
 
< 0.1%
5.991464547 1
 
< 0.1%
5.913503006 1
 
< 0.1%
5.902633333 1
 
< 0.1%
5.899897354 24
0.1%
5.897153868 1
 
< 0.1%
5.886104031 5
 
< 0.1%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct393
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.827712
Minimum0
Maximum629
Zeros9182
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size725.4 KiB
2024-06-19T19:02:51.721501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q324
95-th percentile116
Maximum629
Range629
Interquartile range (IQR)23

Descriptive statistics

Standard deviation45.190521
Coefficient of variation (CV)1.8965531
Kurtosis18.944347
Mean23.827712
Median Absolute Deviation (MAD)5
Skewness3.64035
Sum1106273
Variance2042.1832
MonotonicityNot monotonic
2024-06-19T19:02:51.996182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9182
19.8%
1 4976
 
10.7%
2 3318
 
7.1%
3 2390
 
5.1%
4 1918
 
4.1%
5 1526
 
3.3%
6 1297
 
2.8%
7 1130
 
2.4%
8 1080
 
2.3%
9 924
 
2.0%
Other values (383) 18687
40.2%
ValueCountFrequency (%)
0 9182
19.8%
1 4976
10.7%
2 3318
 
7.1%
3 2390
 
5.1%
4 1918
 
4.1%
5 1526
 
3.3%
6 1297
 
2.8%
7 1130
 
2.4%
8 1080
 
2.3%
9 924
 
2.0%
ValueCountFrequency (%)
629 1
< 0.1%
607 1
< 0.1%
597 1
< 0.1%
594 1
< 0.1%
576 1
< 0.1%
543 1
< 0.1%
540 1
< 0.1%
510 1
< 0.1%
488 1
< 0.1%
480 1
< 0.1%

last_review
Date

MISSING 

Distinct1754
Distinct (%)4.7%
Missing9182
Missing (%)19.8%
Memory size725.4 KiB
Minimum2011-03-28 00:00:00
Maximum2019-07-08 00:00:00
2024-06-19T19:02:52.277200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:52.570224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct936
Distinct (%)2.5%
Missing9182
Missing (%)19.8%
Infinite0
Infinite (%)0.0%
Mean1.377473
Minimum0.01
Maximum58.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size725.4 KiB
2024-06-19T19:02:52.850397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.04
Q10.19
median0.715
Q32.02
95-th percentile4.67
Maximum58.5
Range58.49
Interquartile range (IQR)1.83

Descriptive statistics

Standard deviation1.6904934
Coefficient of variation (CV)1.2272425
Kurtosis43.099274
Mean1.377473
Median Absolute Deviation (MAD)0.615
Skewness3.1549195
Sum51305.36
Variance2.8577679
MonotonicityNot monotonic
2024-06-19T19:02:53.121497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 886
 
1.9%
0.05 856
 
1.8%
1 828
 
1.8%
0.03 772
 
1.7%
0.04 639
 
1.4%
0.16 638
 
1.4%
0.08 580
 
1.2%
0.09 564
 
1.2%
0.06 557
 
1.2%
0.11 527
 
1.1%
Other values (926) 30399
65.5%
(Missing) 9182
 
19.8%
ValueCountFrequency (%)
0.01 40
 
0.1%
0.02 886
1.9%
0.03 772
1.7%
0.04 639
1.4%
0.05 856
1.8%
0.06 557
1.2%
0.07 453
1.0%
0.08 580
1.2%
0.09 564
1.2%
0.1 438
0.9%
ValueCountFrequency (%)
58.5 1
< 0.1%
27.95 1
< 0.1%
20.94 1
< 0.1%
19.75 1
< 0.1%
17.82 1
< 0.1%
16.81 1
< 0.1%
16.22 1
< 0.1%
16.03 1
< 0.1%
15.78 1
< 0.1%
15.32 1
< 0.1%
Distinct47
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6725037
Minimum1
Maximum327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size725.4 KiB
2024-06-19T19:02:53.398298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile13
Maximum327
Range326
Interquartile range (IQR)1

Descriptive statistics

Standard deviation31.083436
Coefficient of variation (CV)4.6584368
Kurtosis75.60396
Mean6.6725037
Median Absolute Deviation (MAD)0
Skewness8.3505074
Sum309791
Variance966.18001
MonotonicityNot monotonic
2024-06-19T19:02:53.670107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 30677
66.1%
2 6436
 
13.9%
3 2745
 
5.9%
4 1354
 
2.9%
5 808
 
1.7%
6 529
 
1.1%
8 396
 
0.9%
7 390
 
0.8%
327 272
 
0.6%
9 225
 
0.5%
Other values (37) 2596
 
5.6%
ValueCountFrequency (%)
1 30677
66.1%
2 6436
 
13.9%
3 2745
 
5.9%
4 1354
 
2.9%
5 808
 
1.7%
6 529
 
1.1%
7 390
 
0.8%
8 396
 
0.9%
9 225
 
0.5%
10 203
 
0.4%
ValueCountFrequency (%)
327 272
0.6%
232 192
0.4%
121 98
 
0.2%
103 103
 
0.2%
96 186
0.4%
91 91
 
0.2%
87 87
 
0.2%
65 50
 
0.1%
52 104
 
0.2%
50 49
 
0.1%

availability_365
Real number (ℝ)

ZEROS 

Distinct366
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.67685
Minimum0
Maximum365
Zeros17005
Zeros (%)36.6%
Negative0
Negative (%)0.0%
Memory size725.4 KiB
2024-06-19T19:02:54.378940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median40
Q3217
95-th percentile358
Maximum365
Range365
Interquartile range (IQR)217

Descriptive statistics

Standard deviation130.41395
Coefficient of variation (CV)1.1890745
Kurtosis-0.92278392
Mean109.67685
Median Absolute Deviation (MAD)40
Skewness0.80642813
Sum5092077
Variance17007.799
MonotonicityNot monotonic
2024-06-19T19:02:54.665251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17005
36.6%
365 1122
 
2.4%
364 430
 
0.9%
1 397
 
0.9%
89 334
 
0.7%
5 333
 
0.7%
3 296
 
0.6%
179 273
 
0.6%
90 270
 
0.6%
2 254
 
0.5%
Other values (356) 25714
55.4%
ValueCountFrequency (%)
0 17005
36.6%
1 397
 
0.9%
2 254
 
0.5%
3 296
 
0.6%
4 227
 
0.5%
5 333
 
0.7%
6 240
 
0.5%
7 212
 
0.5%
8 228
 
0.5%
9 187
 
0.4%
ValueCountFrequency (%)
365 1122
2.4%
364 430
 
0.9%
363 215
 
0.5%
362 150
 
0.3%
361 101
 
0.2%
360 95
 
0.2%
359 127
 
0.3%
358 160
 
0.3%
357 83
 
0.2%
356 73
 
0.2%

Interactions

2024-06-19T19:02:36.956470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:09.745688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:12.324871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:15.059732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:19.530595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:22.435430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:25.016157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:27.625495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:31.105309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:34.363361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:37.213856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:10.006589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:12.604801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:15.438951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:20.134075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:22.703615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:25.279067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:28.169544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:31.430603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:34.629660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:37.461795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:10.257796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:12.839844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:15.742373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:20.386997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:22.952443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:25.560360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:28.413309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:31.830121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:34.887007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:37.732925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:10.501584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:13.097265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:16.100443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:20.638231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:23.197854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:25.801617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:28.730267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:32.248059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:35.129261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:38.001592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:10.758773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:13.337351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:16.468416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:20.885538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:23.458215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:26.047184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:29.080594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:32.656557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:35.374761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:38.257517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:11.026413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:13.600315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:16.812392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:21.144319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:23.729528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:26.309855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:29.467618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:33.076413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:35.650621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:38.514246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:11.278709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:13.845899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:17.160431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:21.400504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:23.989892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:26.581341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:29.795695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:33.337899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:35.916201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:38.767961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:11.541674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:14.102538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:17.553984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:21.671293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:24.243960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:26.826804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:30.124080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:33.604820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:36.172155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:39.034581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:11.802430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:14.377523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:18.631133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:21.920134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:24.498738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:27.076788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:30.419605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:33.852599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:36.416986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:39.289155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:12.069341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:14.688537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:19.101847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:22.174701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:24.760432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:27.335543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:30.793234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:34.109664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-19T19:02:36.685513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-06-19T19:02:54.918228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
availability_365calculated_host_listings_counthost_ididlatitudelongitudeminimum_nightsneighbourhood_groupnumber_of_reviewspricereviews_per_monthroom_type
availability_3651.0000.4110.1660.158-0.0150.0890.0740.0860.2530.0540.4010.093
calculated_host_listings_count0.4111.0000.1450.135-0.0000.0680.0640.0880.065-0.1230.1550.097
host_id0.1660.1451.0000.5520.0440.125-0.1360.103-0.122-0.1000.2620.098
id0.1580.1350.5521.000-0.0010.084-0.0610.065-0.306-0.0410.3550.071
latitude-0.015-0.0000.044-0.0011.0000.0430.0200.539-0.0390.130-0.0260.109
longitude0.0890.0680.1250.0840.0431.000-0.1190.6520.072-0.4230.1290.146
minimum_nights0.0740.064-0.136-0.0610.020-0.1191.0000.080-0.1800.103-0.2930.141
neighbourhood_group0.0860.0880.1030.0650.5390.6520.0801.000-0.0150.1140.0460.115
number_of_reviews0.2530.065-0.122-0.306-0.0390.072-0.180-0.0151.000-0.0260.7140.022
price0.054-0.123-0.100-0.0410.130-0.4230.1030.114-0.0261.000-0.0200.503
reviews_per_month0.4010.1550.2620.355-0.0260.129-0.2930.0460.714-0.0201.0000.028
room_type0.0930.0970.0980.0710.1090.1460.1410.1150.0220.5030.0281.000

Missing values

2024-06-19T19:02:40.043788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-19T19:02:40.614328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-19T19:02:41.112424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
02539Clean & quiet apt home by the park2787JohnBrooklynKensington40.64749-73.97237Private room1490.00000092018-10-190.216365
12595Skylit Midtown Castle2845JenniferManhattanMidtown40.75362-73.98377Entire home/apt2250.000000452019-05-210.382355
23647THE VILLAGE OF HARLEM....NEW YORK !4632ElisabethManhattanHarlem40.80902-73.94190Private room1501.0986120NaTNaN1365
33831Cozy Entire Floor of Brownstone4869LisaRoxanneBrooklynClinton Hill40.68514-73.95976Entire home/apt890.0000002702019-07-054.641194
45022Entire Apt: Spacious Studio/Loft by central park7192LauraManhattanEast Harlem40.79851-73.94399Entire home/apt802.30258592018-11-190.1010
55099Large Cozy 1 BR Apartment In Midtown East7322ChrisManhattanMurray Hill40.74767-73.97500Entire home/apt2001.098612742019-06-220.591129
65121BlissArtsSpace!7356GaronBrooklynBedford-Stuyvesant40.68688-73.95596Private room603.806662492017-10-050.4010
75178Large Furnished Room Near B'way8967ShunichiManhattanHell's Kitchen40.76489-73.98493Private room790.6931474302019-06-243.471220
85203Cozy Clean Guest Room - Family Apt7490MaryEllenManhattanUpper West Side40.80178-73.96723Private room790.6931471182017-07-210.9910
95238Cute & Cozy Lower East Side 1 bdrm7549BenManhattanChinatown40.71344-73.99037Entire home/apt1500.0000001602019-06-091.334188
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
4888536482809Stunning Bedroom NYC! Walking to Central Park!!131529729KendallManhattanEast Harlem40.79633-73.93605Private room750.6931470NaTNaN2353
4888636483010Comfy 1 Bedroom in Midtown East274311461ScottManhattanMidtown40.75561-73.96723Entire home/apt2001.7917590NaTNaN1176
4888736483152Garden Jewel Apartment in Williamsburg New York208514239MelkiBrooklynWilliamsburg40.71232-73.94220Entire home/apt1700.0000000NaTNaN3365
4888836484087Spacious Room w/ Private Rooftop, Central location274321313KatManhattanHell's Kitchen40.76392-73.99183Private room1251.3862940NaTNaN131
4888936484363QUIT PRIVATE HOUSE107716952MichaelQueensJamaica40.69137-73.80844Private room650.0000000NaTNaN2163
4889036484665Charming one bedroom - newly renovated rowhouse8232441SabrinaBrooklynBedford-Stuyvesant40.67853-73.94995Private room700.6931470NaTNaN29
4889136485057Affordable room in Bushwick/East Williamsburg6570630MarisolBrooklynBushwick40.70184-73.93317Private room401.3862940NaTNaN236
4889236485431Sunny Studio at Historical Neighborhood23492952Ilgar & AyselManhattanHarlem40.81475-73.94867Entire home/apt1152.3025850NaTNaN127
488933648560943rd St. Time Square-cozy single bed30985759TazManhattanHell's Kitchen40.75751-73.99112Shared room550.0000000NaTNaN62
4889436487245Trendy duplex in the very heart of Hell's Kitchen68119814ChristopheManhattanHell's Kitchen40.76404-73.98933Private room901.9459100NaTNaN123